Engineering Manager, Borglet Machine Learning

Google Google · Big Tech · Sunnyvale, CA +1

Google is seeking an Engineering Manager for their Borglet Machine Learning team in Sunnyvale, CA. This role involves managing a team of Software Engineers focused on designing, implementing, and analyzing low-level computer systems and their interactions with the kernel and hardware. The team contributes to Google's internal infrastructure and new Cloud and ML business, managing resources at scale. The manager will set team priorities, establish expectations, develop technical roadmaps, guide systems designs, and review code. The role requires a Bachelor's degree or equivalent experience, 8 years in software development, 3 years in large-scale infrastructure or distributed systems, and 3 years in technical leadership, including 2 years in people management.

What you'd actually do

  1. Set and communicate team priorities that support the broader organization's goals, aligning strategy, processes, and decision-making across teams.
  2. Establish clear expectations with individuals based on their level and role and aligned to the broader organization's goals, and meet regularly with them to discuss performance, development, provide feedback and coaching.
  3. Develop the mid-term technical goal and roadmap for your team(s), evolving it to anticipate future requirements and infrastructure needs.
  4. Design, guide and vet systems designs within the scope of the broader area, and write product or system development code to solve ambiguous problems.
  5. Review code developed by other engineers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).

Skills

Required

  • software development
  • large-scale infrastructure
  • distributed systems
  • networks
  • compute technologies
  • storage
  • hardware architecture
  • technical leadership
  • people management
  • team leadership

Nice to have

  • system software
  • complex multi-component software systems
  • system software products
  • C/C++ programming
  • Linux kernel interface
  • containers
  • reliability
  • efficiency
  • scale
  • performance analysis
  • tuning
  • data analysis
  • SQL

What the JD emphasized

  • managing fastest-growing resources
  • managing a team of Software Engineers focused on designing, implementing, and analyzing low-level computer systems and their interactions with the kernel and hardware
  • delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity